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Conserving energy and efficient power systems are very important for us to reduce pollution levels and also reduces wasted fuel resources which are already depleting on the planet. In power operation system, the potential of energy conservation and also much less emission of greenhouse gas because of the wise usage of cleaner non-renewable fuels burned in combined heat and power (CHP) models like the natural gas which provide them benefit from the usual electric power systems. Mixed generator systems have been widely employed by the industry. The industry which requires both power and heat can supply the demands with cogeneration of heat-power systems. Cogeneration (CHP) systems could be constructed in cities and used in the form of distributed electricity sources. To get the optimal usage of CHP devices, economic load dispatch (ED) should be requested more for the process of energy conservation. Economic load dispatch plays a vital role and a large number of different approaches and methods have been used in solving such kind of problems. The methods like lambda-iteration and Gradient are used for finding out the optimized solution of nonlinear problem. The purpose of this thesis is to utilize the algorithmic optimization approaches like particle swarm optimization (PSO), genetic algorithm (GA) and PSO-GA. In this work, the method of PSO-GA Optimization is used to find out the minimized cost at four of the generating units of heat and power. The base of the work is already published where in the loss coefficients are also presented with max-min cost function and power limit. This work is implemented in the MATLAB simulation environment. The work starts by initializing the load/power and then generators load power flow. The generators are allocated to initialize the cost and this cost is optimized by PSO with GA. At the end the experimental results of GA ,PSO and PSO-GA algorithm is equated with each other and it seems better convergence is achieved by PSO-GA Algorithm.

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Cellulose is an abundant plant biomass and a renewable source of energy in the ecosphere. The breakdown of cellulose occurs via the cellulase enzyme, which is commonly produced by microbes. This study aimed to optimize the fermentation parameters for enhanced cellulase production. Standardized parameters include isolation and screening of cellulase-producing bacteria (CPB), production of an enzyme, biochemical and molecular identification of bacterial isolate, optimization of cultural parameters, and application in wash performance. A total of 581 bacterial strains were isolated from soil samples, of which 16 isolates formed zones of hydrolysis on carboxymethylcellulose (CMC) agar media and were categorized as CPB. Based on maximum hydrolysis zone formation, three isolates, Krishi Vigyan Kendra-5 (KVK-5), Greenhouse-4 (GA-4), and Medicinal Garden-5 (MG-5) were chosen for bacterial cellulase production (BCP), with the isolate MG-5 proving to be the best cellulase producer (1.75 ± 0.01 U ml-1). Based on 16S rRNA gene sequencing the isolate MG-5 was identified as Enterococcus durans. The optimized parameters for the production of the cellulolytic enzyme were an incubation period of 48 h, CMC (carbon source), and yeast extract (nitrogen source) at a concentration of 1.5% w/v, pH 7, 45 °C, 1.5% v/v inoculum size and 100 rpm. Optimum conditions resulted in a 1.92-fold increase (3.36 U ml-1) in cellulase activity. Cellulase enzyme when used with detergent (Surf Excel), resulted in more efficient removal of chocolate stains on cotton fabric. This is the first report of Enterococcus durans producing cellulolytic enzymes. The analysis of cellulase in stain removal provides valuable evidence regarding the application of this enzyme in laundry cleaning.